Stochastic Operation of a Solar-Powered Smart Home: Capturing Thermal Load Uncertainties

Esmaeil Ahmadi, Younes Noorollahi, Behnam Mohammadi-Ivatloo and Amjad Anvari-Moghaddam
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Esmaeil Ahmadi: Energy Economics Laboratory, Department of Socio-Environmental Energy Science, Graduate School of Energy Science, Kyoto University, Kyoto 606-8501, Japan
Younes Noorollahi: Renewable Energy and Environmental Department, Faculty of New Sciences and Technologies, University of Tehran, Tehran 15119-43943, Iran
Behnam Mohammadi-Ivatloo: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5157944533, Iran
Amjad Anvari-Moghaddam: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5157944533, Iran

Sustainability, 2020, vol. 12, issue 12, 1-18

Abstract: This study develops a mixed-integer linear programming (MILP) model for the optimal and stochastic operation scheduling of smart buildings. The aim of this study is to match the electricity demand with the intermittent solar-based renewable resources profile and to minimize the energy cost. The main contribution of the proposed model addresses uncertainties of the thermal load in smart buildings by considering detailed types of loads such as hot water, heating, and ventilation loads. In smart grids, buildings are no longer passive consumers. They are controllable loads, which can be used for demand-side energy management. Smart homes, as a domain of Internet of Things (IoT), enable energy systems of the buildings to operate as an active load in smart grids. The proposed formulation is cast as a stochastic MILP model for a 24-h horizon in order to minimize the total energy cost. In this study, Monte Carlo simulation technique is used to generate 1000 random scenarios for two environmental factors: the outdoor temperature, and solar radiation. Therefore in the proposed model, the thermal load, the output power of the photovoltaic panel, solar collector power generation, and electricity load become stochastic parameters. The proposed model results in an energy cost-saving of 20%, and a decrease of the peak electricity demand from 7.6 KWh to 4.2 KWh.

Keywords: smart home; solar renewable; thermal load; stochastic operation; energy storage (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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